Trends and Patterns in Fraud and Cybercrime Complaints in Canada: A 2021-2024 Analysis
Seminar Paper in the field of Applied Macroeconomics
Shahzain Khan - (MSc. Economics)
23-June-2024
Introduction
In today’s digital age, fraud has become a pervasive risk, infiltrating various aspects of daily life. The fraud landscape is vast and growing, so understanding the extent and impact of this fraudulent activity on individuals and the wider economy is important. This Notebook explores the findings of a study by Global Fraud Alliance (GASA) and Feedzai1.
Research Objectives
Through this research, we aim to:
- Demographic Analysis: Understand the demographic distribution of fraud victims, including age, sex, and education.
- Frequency and Trends: Examine the frequency with which Canadians experience fraud and identify any notable trends over the past year.
- Scam Channels: Identify the main channels used for scams, such as phone, email, and social media platforms.
- Financial and Emotional Impact: Look at the financial loss on the victims and the emotional damage caused by this fraud.
- Reporting and Government Response: Examine the reporting behavior of fraud victims and their satisfaction with government efforts to combat fraud.
- Preventive Measures: Highlight the methods individuals use to avoid fraud and discuss their effectiveness.
Current Literature
Fraud and methods of dissemination
A growing body of literature highlights the alarming prevalence of fraud in Canada. According to a report by the Canadian Anti-Fraud Center (CAFC), Canadians reported more than 68,000 cases of fraud in 2023 alone with financial losses in excess of 531 Million CAD (Global Anti-Scam Alliance, 2023).
Mental and Emotional Influences
The psychological and emotional toll of cheating is significant and often overlooked. Research has shown that victims of fraud experience a range of negative emotions including shame, guilt and anxiety by (Button et al., 2014). Survey found that 47% of fraud victims in Canada reported severe emotional impact, and that these non-financial behaviors need to be addressed with support systems (Feedzai and Global Fraud Alliance, 2024).
Economic Impact
The economic losses caused by the fraud are enormous. An analysis estimated an average loss of 2,406 CAD per person, representing a loss of 13 Billion CAD per year or 0.5% of Canada’s GDP (Feedzai and Global Fraud Alliance, 2024). The analysis, alongside various studies (Cross, 2019) show the enormous economic burden of personal and economic fraud.
Reporting & Regulation
Despite the high level of fraud, the number of reports remains low. Some examinations found that 69% of fraud victims do not report their experiences to law enforcement, often due to uncertainty of where to report, complex procedures and fear of repercussions. This underreporting complicates efforts to quantify and combat fraud (Feedzai and Global Fraud Alliance, 2024). A Study emphasizes the importance of systematic reporting (Parti & Tahir, 2023).
Data Exploration
Data Source
Dataset has been sourced from the Canadian Anti-Fraud Center’s Fraud Reporting System database2. Data was gathered by Canadian Anti-Fraud Center’s through their Fraud Reporting System and primarily from public submission. The accuracy of the data depends on various factors i.e. completeness, reliability of the information provided by the user while submitting the information.
Here are the definition and description of abbreviation used in the dataset
| Variables | Definition |
|---|---|
| Date_Received | When the complaint was reported |
| Complaint_Received_Type | A report received through the CAFC’s Online Reporting System |
| Province_State | The province or territory where the scam occurred |
| Fraud_and_Cybercrime_Thematic_Categories | The type of fraud reported by the victim (selected from a drop-down list or described during a telephone report to a CAFC intake analyst) |
| Solicitation_Method | The initial method of contact between the fraudster and victim |
| Language_of_Correspondence | The language used during the fraud |
| Complaint_Type | Type of complaint received by CAFC (e.g., Email, Phone) |
| Number_of_Victims | Total number of victims associated with the reported instance(s) of fraud |
| Dollar_Loss | Total amount of money lost due to the instance(s) of fraud |
Data Preparation
To prepare data for for analysis, several measures were undertaken. Following are the step had been taken for data cleansing:
- Renamed all columns for improved readability, addressing columns that were not encoded in UTF-8
- Removed redundant columns i.e. columns contains French language translation
- Retained only columns containing values relevant for analysis
Here is the list of all columns including expected values format
## $Date_Received
## [1] "^[0-9]{4}-[0-9]{2}-[0-9]{2}$"
##
## $Complaint_Received_Type
## [1] "CAFC Website" "NCFRS" "Email" "Phone" "Mail"
## [6] "In Person" "Intercepted" "Message"
##
## $Province_State
## [1] "Saskatchewan" "Quebec"
## [3] "Ontario" "British Columbia"
## [5] "Yukon" "Alberta"
## [7] "Manitoba" "Prince Edward Island"
## [9] "Newfoundland And Labrador" "Nova Scotia"
## [11] "New Brunswick" "North West Territories"
## [13] "Nunavut"
##
## $Fraud_and_Cybercrime_Thematic_Categories
## [1] "Merchandise"
## [2] "Identity Fraud"
## [3] "Phishing"
## [4] "Vendor Fraud"
## [5] "Spear Phishing"
## [6] "Extortion"
## [7] "Emergency (Jail, Accident, Hospital, Help)"
## [8] "Job"
## [9] "Prize"
## [10] "Personal Info"
## [11] "Counterfeit Merchandise"
## [12] "Service"
## [13] "GRANT"
## [14] "Collection Agency"
## [15] "Bank Investigator"
## [16] "Investments"
## [17] "Romance"
## [18] "Spoofing"
## [19] "Loan"
## [20] "Unauthorized Charge"
## [21] "Charity / Donation"
## [22] "Foreign Money Offer"
## [23] "Office Supplies"
## [24] "Health"
## [25] "Timeshare"
## [26] "Psychics"
## [27] "False Billing"
## [28] "Vacation"
## [29] "Recovery Pitch"
## [30] "Survey"
## [31] "Fraudulent Cheque"
## [32] "Directory"
## [33] "Pyramid"
## [34] "Modem-Hijacking"
## [35] "Telecom Fraud"
## [36] "Credit Card"
##
## $Solicitation_Method
## [1] "Email" "Text message"
## [3] "Direct call" "Internet-social network"
## [5] "Internet" "Mail"
## [7] "Door to door/in person" "Print"
## [9] "Video Call" "Television"
## [11] "Radio"
##
## $Gender
## [1] "Male" "Female"
##
## $Language_of_Correspondence
## [1] "English" "French"
##
## $Victim_Age_Range
## [1] "'1 - 9" "'10 - 19" "'20 - 29"
## [4] "'30 - 39" "'40 - 49" "'50 - 59"
## [7] "'60 - 69" "'70 - 79" "'80 - 89"
## [10] "'90 - 99" "'100 +" "'Business / Entreprise"
##
## $Complaint_Type
## [1] "Attempt" "Victim"
##
## $Dollar_Loss
## [1] "^\\$\\d+\\.\\d{2}$"
##
## $Country
## [1] "Canada"
##
## $Number_of_Victims
## [1] 0 1Finally, here are our final dataset with all expected values and without any missing data. In the final dataset we have 84,427 number of rows and 12 number of columns
It is noticeable that there are numerous fields in which there are no values. This is illustrated in the following table:
Results
Gender-Based Analysis
Hypotheses
- H1: There is a significant difference in the distribution of fraud and cybercrime complaints based on gender.
- H2: Certain types of scams are more prevalent among specific genders.
Visual Representation:
Gender Distribution of Affected Individuals
Figure 1: Gender distribution of affected individuals
Most Occurred Scams by Categories
Figure 2: Relationship between Gender & Most Occurred Scams/Fraud by Categories
Least Occurred Scams by Categories
Figure 3: Relationship between Gender & Least Occurred Scams/Fraud by Categories
Analytical Analysis
- X-squared Value (21.9): This is the test statistic for the chi-square test. A higher value indicates a stronger association between the variables.
- Degrees of Freedom (1): This indicates the number of independent comparisons in the chi-square test.
- p-value (2.88e-06): This is the probability of observing the test results under the null hypothesis (no association between the variables). A p-value less than 0.05 typically indicates statistical significance.
Analytical Analysis Results:
Chi Square Result
| Variables | Values |
|---|---|
| Degrees of freedom (df) | 1 |
| X-squared | 2.19e+01 |
| p-value | 2.88e-06 |
Regression Analysis for Male
Figure 4: Regression Analysis for Male
Regression Analysis for Female
Figure 5: Regression Analysis for Female
Age-Based Analysis
Hypotheses
- H3: Different age groups experience varying types and frequencies of fraud and cybercrime complaints.
- H4: The monetary loss due to fraud and cybercrime varies significantly with age and gender.
Visual Representation:
Victim by Age and Complaint Type
Figure 6: Victim by Age and Complaint Type
Monetary Loss by Age and Gender
Figure 7: Monetary Loss by Age and Gender
Analytical Analysis
Analytical Analysis Results:
Frequencies of different Fraud Types Across Age Groups - ANOVA test
- Victim Age Range:
- The p-value is 1.83e-15, which is significantly less than the common significance level of 0.05.
- This indicates that there are statistically significant differences in financial losses across different age ranges of victims.
- Fraud Type:
- The p-value is 1.46e-39, which is also significantly less than 0.05.
- This indicates that there are statistically significant differences in financial losses across different types of fraud.
- Overall:
- Both Victim_Age_Range and Fraud_Type are significant predictors of financial loss.
- This suggests that age and the type of fraud are important factors influencing the amount of financial loss experienced by victims.
- The significant F values and very low p-values imply that variations in financial losses are significantly influenced by the victim’s age range and the type of fraud they encountered. These findings can guide targeted measures and policies to address specific groups and types of fraud more effectively.
| Variable | Df | Sum Sq | Mean Sq | F value | Pr(>F) |
|---|---|---|---|---|---|
| Victim_Age_Range | 10 | 1.22e+07 | 1.22e+06 | 1.06e+01 | 1.83e-15 |
| Fraud_Type | 32 | 4.62e+07 | 1.45e+06 | 1.25e+01 | 1.46e-39 |
| Residuals | 320 | 3.7e+07 | 1.16e+05 |
Relationship between Age, Gender, and Dollar_Loss
- Victim_Age_Range:
- F value: High FA indicates significant differences in DollarLoss across age ranges.
- p-value (pA): If pA < 0.05, age range significantly affects DollarLoss.
- Gender:
- F value: High FB indicates significant differences in DollarLoss between genders.
- p-value (pB): If pB < 0.05, gender significantly affects DollarLoss.
- Interaction (Victim_Age_Range):
- F value: High FAB indicates a significant interaction effect between age and gender on DollarLoss.
- p-value (pAB): If pAB < 0.05, the interaction significantly affects DollarLoss.
- Interpretation:
- Age Range: Significant (pA < 0.05). Different age groups experience different levels of DollarLoss.
- Gender: Significant (pB < 0.05). Financial losses vary significantly between male and female victims.
- Interaction: Significant (pAB < 0.05). The effect of age on DollarLoss varies depending on gender.
Figure 8: Analyzing the Relationship between Age, Gender, and Dollar Loss
The analysis indicates that both age and gender significantly influence financial losses due to fraud. Furthermore, the interaction between age and gender also plays a significant role. These findings highlight the need for targeted strategies to address vulnerabilities specific to different age and gender groups in combating fraud and cybercrime.
Regional Analysis
Hypotheses
- H5: The frequency of fraud and cybercrime complaints varies significantly across different provinces in Canada.
- H6: Certain types of fraud are more prevalent in specific provinces.
Visual Representation:
Fraud Incidents by Provinces in Canada
Figure 9: Fraud Incidents by Provinces in Canada
Fraud Distribution by Province and Category
Figure 10: Fraud distribution by Province and Category
Analytical Analysis
Intercept: The log odds of reporting a scam when all predictors are at the reference level.
Victim_Age_Range: A positive coefficient (0.256) indicates that with an increase in age range, the log odds of reporting a scam increase.
Gender Male: A positive coefficient (0.398) indicates that males are more likely to report a scam compared to the reference gender.
Type Of Fraud Y: A positive coefficient (0.562) suggests that this type of fraud is more likely to be reported.
Province State Z: A negative coefficient (-0.173) indicates that residents of this province/state are less likely to report scams compared to the reference province/state. Significance:
The p-values for all predictors are less than 0.05, indicating that they significantly affect the likelihood of reporting scams.
The logistic regression analysis shows that age, gender, type of fraud, and province/state significantly influence the likelihood of reporting scams to law enforcement. This information can help in understanding the reporting behaviour and designing targeted interventions to encourage scam reporting among different demographic groups and regions.
Analytical Analysis Results:
Number of Complaints Across Provinces - ANOVA test
| Variables | Df | Sum Sq | Mean Sq | F value | Pr(>F) |
|---|---|---|---|---|---|
| Province_State | 1.2e+01 | 1.5e+02 | 1.3e+01 | 5.1e+01 | 3.4e-123 |
| Residuals | 8.4e+04 | 2.1e+04 | 2.5e-01 |
Number of Victim and Outcome of Fraud
Figure 11: Relationship between Number of Victim and Outcome of Fraud
Temporal Analysis
Hypotheses
- H7: The frequency of fraud and cybercrime complaints shows significant variation over time, with certain periods experiencing higher incidences.
- H8: There are specific months when no fraud and cybercrime complaints are filed, indicating potential reporting patterns or external influences.
Visual Representation:
Individual Scams Over Time Regression Line
Figure 12: Individual Scams Over Time
Individual Scams Over Time With Multiple Regression Lines
Figure 13: Individual Scams Over Time
Months when no Incident Reported
Figure 14: Months when no Incident Reported
Analytical Analysis
Analytical Analysis Results:
Distribution of Complaint by Month
Figure 15: Distribution of Complaint by Month
Discussion
- Demographic Factors
- Age:
- Older individuals are more likely to report fraud. This suggests the need for targeted outreach to younger demographics to increase their reporting rates.
- Gender:
- Males are more likely to report fraud than females. This indicates that females might require more support and encouragement to report fraud incidents.
- Age:
- Regional Variations
- Province/State:
- Significant differences in financial losses and reporting rates across provinces/states suggest that regional factors such as local policies, awareness programs, and law enforcement practices play a crucial role.
- Tailored strategies are needed for regions with lower reporting rates and higher financial losses.
- Province/State:
- Policy Implications
- Targeted Awareness Campaigns:
- Develop campaigns aimed at younger individuals and females to encourage reporting and increase awareness of fraud and cybercrime.
- Regional Strategies:
- Implement region-specific policies and support systems to address the unique challenges and patterns of fraud in different provinces/states.
- Enhanced Reporting Mechanisms:
- Simplify and promote reporting processes to make it easier for victims to report fraud incidents, ensuring consistent support across all regions.
- Support Services:
- Provide targeted support services for demographics less likely to report fraud, such as younger individuals and females, to build confidence in the reporting system.
- Targeted Awareness Campaigns:
- Areas for Future Research
- Behavioral Studies:
- Investigate the behavioral factors influencing the likelihood of reporting fraud among different demographics.
- Regional Analysis:
- Conduct in-depth studies to understand the regional factors contributing to variations in financial losses and reporting rates.
- Impact of Awareness Programs:
- Evaluate the effectiveness of current fraud awareness and prevention programs and identify areas for improvement.
- Longitudinal Studies:
- Perform longitudinal studies to track changes in fraud patterns, reporting rates, and the effectiveness of interventions over time.
- Behavioral Studies:
Conclusion
The comprehensive analysis of fraud and cybercrime complaints in Canada from 2021 to 2024 reveals significant insights into the demographic and regional patterns of victimization and reporting behaviours. Through a series of statistical tests, including ANOVA and logistic regression, we have identified critical factors that influence financial losses and the likelihood of reporting fraud.
This study provides a detailed analysis of the demographic and regional patterns in fraud and cybercrime complaints in Canada, highlighting significant factors that influence financial losses and reporting behaviours. The findings underscore the importance of targeted awareness campaigns, regional strategies, and enhanced reporting mechanisms to combat fraud effectively. Future research should focus on understanding the behavioural aspects of reporting and the regional factors contributing to fraud patterns to inform more effective policy measures.